Prototype Testing

pacman::p_load(sf, tmap, tidyverse,readxl,urca,tseries,plotly,ggdist,ggridges,lubridate,knitr)

mpsz <- st_read(dsn = "data/geospatial", 
                layer = "MP14_PLNG_AREA_WEB_PL")

Reading layer `MP14_PLNG_AREA_WEB_PL' from data source 
  `/Users/johsuan/johsuanh/ISSS608-VAA-GroupProject/ShinyApp/data/geospatial' 
  using driver `ESRI Shapefile'
Simple feature collection with 55 features and 12 fields
Geometry type: MULTIPOLYGON
Dimension:     XY
Bounding box:  xmin: 2667.538 ymin: 15748.72 xmax: 56396.44 ymax: 50256.33
Projected CRS: SVY21

age <- read_csv("data/ResidentPopulationbyPlanningAreaSubzoneofResidenceAgeGroupandFloorAreaofResidenceCensusofPopulation2020.csv")

Rows: 388 Columns: 121
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (121): Number, Total1_Total, Total1_0_4, Total1_5_9, Total1_10_14, Total...

ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

income <- read_excel("data/ResidentHouseholdsbyPlanningAreaofResidenceandMonthlyHouseholdIncomefromWorkCensusOfPopulation2020.xlsx",sheet = "sheet1",range="A11:U43")

age <- age %>%
  filter(grepl("Total", Number, ignore.case = TRUE)) %>%  
  select(1:21) %>%
  replace(. == "-", NA) %>%
  mutate(across(2:21, as.numeric)) %>%
  mutate(Aged = rowSums(select(., 16:21), na.rm = TRUE)) %>%
  mutate(PA = sub(" - Total.*", "", Number))%>%
  select(c("PA","Aged","Total1_Total"))%>%
  mutate(`Aged%` = round(Aged/Total1_Total*100,0),
         PA = toupper(PA))

age <- left_join(mpsz,age,by = c("PLN_AREA_N" = "PA"))

income <- income %>%
  mutate(across(2:21, as.numeric))
income_proportion <- income %>%
  mutate(across(3:21, ~ round(. / income[[2]] * 100, 2))) %>%
  mutate(`LowerIncome%`=rowSums(select(.,3:6), na.rm = TRUE))
low_income <- income_proportion %>%
  select(c(`Planning Area of Residence`,`LowerIncome%`))%>%
  mutate(`Planning Area of Residence` = toupper(`Planning Area of Residence`))

low_income <- left_join(mpsz,low_income,by = c("PLN_AREA_N" = "Planning Area of Residence"))

# Ensure both layers have the same CRS
low_income <- st_transform(low_income, st_crs(age))

# Set the tmap mode to view (interactive)
tmap_mode("view")

ℹ tmap mode set to "view".

# Create the map with multiple layers
tm_shape(age) +
  tm_fill(col = "Aged%", 
          palette = "Blues", 
          title = "Aged Population(%)",
          alpha = 0.9,
          style = "quantile") +
  tm_borders(col = "white", lwd = 0.5, alpha = 0.5) +
tm_shape(low_income) + 
  tm_fill(col = "LowerIncome%", 
          palette = "YlOrRd", 
          title = "IncomeBelow:3000(%)", 
          style = "quantile",
          n = 5,
          alpha = 0.6) +
  tm_borders(col = "darkgrey", lwd = 0.5, alpha = 0.5) +
tm_basemap(server = "CartoDB.Positron") +
tm_layout(title = "Aged and Low-Income Population Across Singapore", 
          legend.outside = TRUE,
          legend.outside.position = "right")

── tmap v3 code detected ───────────────────────────────────────────────────────
[v3->v4] `tm_polygons()`: instead of `style = "quantile"`, use fill.scale =
`tm_scale_intervals()`.
ℹ Migrate the argument(s) 'style', 'palette' (rename to 'values') to
  'tm_scale_intervals(<HERE>)'[v3->v4] `tm_polygons()`: use 'fill' for the fill color of polygons/symbols
(instead of 'col'), and 'col' for the outlines (instead of 'border.col').[v3->v4] `tm_polygons()`: use `fill_alpha` instead of `alpha`.[v3->v4] `tm_polygons()`: migrate the argument(s) related to the legend of the
visual variable `fill` namely 'title' to 'fill.legend = tm_legend(<HERE>)'[v3->v4] `tm_borders()`: use `fill_alpha` instead of `alpha`.[v3->v4] `tm_polygons()`: use `fill_alpha` instead of `alpha`.[v3->v4] `tm_polygons()`: migrate the argument(s) related to the legend of the
visual variable `fill` namely 'title' to 'fill.legend = tm_legend(<HERE>)'[v3->v4] `tm_borders()`: use `fill_alpha` instead of `alpha`.[v3->v4] `tm_layout()`: use `tm_title()` instead of `tm_layout(title = )`[cols4all] color palettes: use palettes from the R package cols4all. Run
`cols4all::c4a_gui()` to explore them. The old palette name "Blues" is named
"brewer.blues"Multiple palettes called "blues" found: "brewer.blues", "matplotlib.blues". The first one, "brewer.blues", is returned.
[cols4all] color palettes: use palettes from the R package cols4all. Run
`cols4all::c4a_gui()` to explore them. The old palette name "YlOrRd" is named
"brewer.yl_or_rd"Multiple palettes called "yl_or_rd" found: "brewer.yl_or_rd", "matplotlib.yl_or_rd". The first one, "brewer.yl_or_rd", is returned.

pacman::p_load(ggdist,ggridges,lubridate,knitr)

station<-readRDS("data/station_data.rds")

pacman::p_load(dplyr, lubridate)

#station <- station %>%mutate(across(where(is.character), as.numeric)) 

str(station)

tibble [22,230 × 15] (S3: tbl_df/tbl/data.frame)
 $ station                    : Factor w/ 10 levels "Admiralty","Ang Mo Kio",..: 9 9 9 9 9 9 9 9 9 9 ...
 $ year                       : num [1:22230] 2019 2019 2019 2019 2019 ...
 $ month                      : num [1:22230] 1 1 1 1 1 1 1 1 1 1 ...
 $ day                        : num [1:22230] 1 2 3 4 5 6 7 8 9 10 ...
 $ daily_rainfall_total_mm    : num [1:22230] 0 0 0 0 6.2 0 0 14.9 0 0 ...
 $ highest_30_min_rainfall_mm : num [1:22230] 0 0 0 0 NA 0 0 NA 0 0 ...
 $ highest_60_min_rainfall_mm : num [1:22230] 0 0 0 0 NA 0 0 NA 0 0 ...
 $ highest_120_min_rainfall_mm: num [1:22230] 0 0 0 0 NA 0 0 NA 0 0 ...
 $ mean_temperature_c         : num [1:22230] 29.5 30 30.3 30.4 29 29.1 29.4 28.6 29.1 29.2 ...
 $ maximum_temperature_c      : num [1:22230] 34.2 33.8 33.1 33.5 34 32.7 33.1 31.6 32.7 33 ...
 $ minimum_temperature_c      : num [1:22230] 26.1 25.8 26.7 26.9 26 26.2 26 26 25.8 25.3 ...
 $ mean_wind_speed_km_h       : num [1:22230] 11.9 13 15.8 11.2 7.2 15.8 16.6 12.6 16.9 16.6 ...
 $ max_wind_speed_km_h        : num [1:22230] 42.5 46.4 48.2 35.3 31.3 38.9 42.5 33.5 42.5 42.5 ...
 $ date                       : Date[1:22230], format: "2019-01-01" "2019-01-02" ...
 $ weekday                    : Ord.factor w/ 7 levels "Sun"<"Mon"<"Tue"<..: 3 4 5 6 7 1 2 3 4 5 ...

ggplot(station, 
       aes(x = mean_temperature_c, y = station, fill = station)) +
  geom_density_ridges(
    scale = 2, 
    rel_min_height = 0.01, 
    alpha = 0.5
  ) +
  labs(title = "Distribution of Temperature Across Stations",
       x = "Mean Temperature (°C)", 
       y = "Station") +
  theme(
    panel.background = element_rect(fill = "#f3f1e9"),
    plot.background = element_rect(fill = "#f3f1e9", color = NA),
    legend.position = "none",
    plot.title = element_text(face = "bold")
  )

Picking joint bandwidth of 0.239

Warning: Removed 1426 rows containing non-finite outside the scale range
(`stat_density_ridges()`).

ggplot(station, 
       aes(x = station,y = mean_temperature_c)) +
  stat_halfeye(
               alpha = 0.5,
               adjust = 0.5,
               justification = -0.1,
               .width = 0,
               fill = "#8AA4FF")+
  geom_boxplot(width = 0.10,
               outlier.shape = NA,
               color="grey50")+
  labs(title ="Distribution of Mean Temperature Across Stations",
       x = "", y="Mean Temperature")+
  coord_flip() +
  theme(panel.background = element_rect(fill = "#ffffff"),
        plot.background = element_rect(fill = "#ffffff",color = NA),
        legend.position = 'none',
        plot.title = element_text(face = "bold",size=13,hjust=0.5))

Warning: Removed 1426 rows containing missing values or values outside the scale range
(`stat_slabinterval()`).

Warning: Removed 1426 rows containing non-finite outside the scale range
(`stat_boxplot()`).

quartile_station_table <- station %>%
  group_by(station) %>%
  summarize(
    Min    = round(min(mean_temperature_c, na.rm = TRUE),2),
    Q1     = round(quantile(mean_temperature_c, probs = 0.25, na.rm = TRUE),2),
    Median = round(median(mean_temperature_c, na.rm = TRUE),2),
    Mean   = round(mean(mean_temperature_c, na.rm = TRUE),2),
    Q3     = round(quantile(mean_temperature_c, probs = 0.75, na.rm = TRUE),2),
    Q4     = round(quantile(mean_temperature_c, probs = 1, na.rm = TRUE),2),
    Max    = round(max(mean_temperature_c, na.rm = TRUE),2)
  )

kable(quartile_station_table)

station	Min	Q1	Median	Mean	Q3	Q4	Max
Admiralty	22.8	27.0	27.8	27.77	28.7	30.8	30.8
Ang Mo Kio	23.0	27.2	28.1	28.04	29.0	31.2	31.2
Changi	23.1	27.4	28.2	28.12	29.0	30.7	30.7
Clementi	22.8	27.0	27.9	27.80	28.7	30.8	30.8
Jurong (West)	22.7	26.8	27.6	27.63	28.6	30.8	30.8
Marina Barrage	23.5	27.9	28.7	28.59	29.5	31.3	31.3
Newton	22.5	27.0	27.8	27.72	28.6	30.4	30.4
Pasir Panjang	23.4	27.5	28.3	28.28	29.1	31.0	31.0
Paya Lebar	23.2	27.6	28.6	28.45	29.4	31.7	31.7
Tai Seng	23.4	27.6	28.5	28.46	29.4	31.3	31.3

pacman::p_load(plotly,ggplot2,dplyr)

# Change to "all" to display all stations
selected_station <- c("Changi", "Marina Barrage","Ang Mo Kio","Clementi","Jurong (West)","Paya Lebar","Newton","Pasir Panjang","Tai Seng","Admiralty"
)

# Filter the data accordingly
station_data <- if ("all" %in% selected_station) {
  station
} else {
  station %>% filter(station %in% selected_station)
}

# Create the plot using the filtered data
p <- ggplot(data = station_data, 
            aes(x = mean_temperature_c,
                y = daily_rainfall_total_mm, 
                color = station)) +
  geom_point(size = 1, alpha = 0.7) +  
  coord_cartesian(ylim = c(0, 150))+
  theme_minimal() +
  labs(x = "Mean Temperature (°C)", y = "Daily Total Rainfall (mm)") +
  theme(
    plot.background = element_rect(fill = "white", color = NA),
    panel.background = element_rect(fill = "white"),
    axis.title = element_text(size = 10, hjust = 0.5),
    axis.text = element_text(size = 8),
    legend.position = "top",
    plot.title = element_text(size = 14, face = "bold", hjust = 0.5)
  )


ggplotly(p)

colnames(station)

 [1] "station"                     "year"                       
 [3] "month"                       "day"                        
 [5] "daily_rainfall_total_mm"     "highest_30_min_rainfall_mm" 
 [7] "highest_60_min_rainfall_mm"  "highest_120_min_rainfall_mm"
 [9] "mean_temperature_c"          "maximum_temperature_c"      
[11] "minimum_temperature_c"       "mean_wind_speed_km_h"       
[13] "max_wind_speed_km_h"         "date"                       
[15] "weekday"                    

# Create the Date column
station <- station %>%
  mutate(date = as.Date(paste(year, month, day, sep = "-"), format = "%Y-%m-%d"))

# Filter only Changi site data
changi_data <- subset(station, station == "Changi")

# Drawing
ggplot(changi_data, aes(x = date, y = mean_temperature_c, color = station, group = station)) +
  geom_line(linewidth = 1, alpha = 0.8) +
  theme_minimal() +
  labs(title = "Mean Wind Speed Over Time at Changi",
       x = "Date",
       y = "Mean temperature (celcius degree)") +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    legend.title = element_blank(),
    legend.position = "top"
  )